{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tarfile\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential, Model, model_from_json\n",
    "from keras.layers import Dense, Conv2D, Activation, MaxPool2D, Flatten, Dropout, BatchNormalization\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion</th>\n",
       "      <th>pixels</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emotion                                             pixels     Usage\n",
       "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
       "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
       "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
       "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
       "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tar = tarfile.open(\"fer2013.tar\")\n",
    "df = pd.read_csv(tar.extractfile(\"fer2013/fer2013.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Training       28709\n",
       "PrivateTest     3589\n",
       "PublicTest      3589\n",
       "Name: Usage, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Usage\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emotion    0\n",
       "pixels     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = df[[\"emotion\", \"pixels\"]][df[\"Usage\"] == \"Training\"]\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28709, 2304), (28709,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['pixels'] = train['pixels'].apply(lambda im: np.fromstring(im, sep=' '))\n",
    "x_train = np.vstack(train['pixels'].values)\n",
    "y_train = np.array(train[\"emotion\"])\n",
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "public_test_df = df[[\"emotion\", \"pixels\"]][df[\"Usage\"]==\"PublicTest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "public_test_df[\"pixels\"] = public_test_df[\"pixels\"].apply(lambda im: np.fromstring(im, sep=' '))\n",
    "x_test = np.vstack(public_test_df[\"pixels\"].values)\n",
    "y_test = np.array(public_test_df[\"emotion\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28709, 48, 48, 1), (3589, 48, 48, 1))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = x_train.reshape(-1, 48, 48, 1)\n",
    "x_test = x_test.reshape(-1, 48, 48, 1)\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28709, 7), (3589, 7))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RSCQSiUQikVgCmFNXHyr3eno8TOS431BV0M3yVCXFBYCAQqk1dRBcSCCvB/DG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\nQlYJPHbZJU0g1yOFrd9/DPj14HWeBbqDzrp+4kIHPDh40JhTSxLg+8b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vv3/gIw+LgZ4g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0u6WNLnJ+3UywzjH3nzzTdL6n3dszLJ7BI5R+p3Z1yTlyT3dY+FQxy0xxfS2Y/vf//7knpfTnaS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\n79y5T7vii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ZIe77pu+8z/94hnXpiF0p1nHqU7z07scbpTelNYAdjj9EYq3ZkPKrh3DrTW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\n13X/2Fp7QNJ1rbX/JeluSZ9cVQ+K4tildKcoVk7pTVGsjtKd4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lrbsjCL3CXpiYn2rChm\nm9KbolgdpTtFsTpKd4rDctgX/67r/kjSH0nSwgzyf3Rd91uttc9KulLSdZLeJ+n6o9VJZkBY2dy6\nhrXF49eloRUjLgHt1kGsD7fccosk6Wtf+9poG7MsZpL8n9hhSbrvvvsk9VaXLJ6LmSOWEl9i/aGH\nHpLUL0RB7LS0OA45i+mkDf669QcPCfuPKx1VTJZJ603XdalMx/+7lQI5z2Itsb65LkhD+eGcxN8T\n0+z7ve51r5PUl06Tel0kxh9Lv+cooAPEhrrVb8eOHZL6RYe+/OUvS5L+6Z/+abQPcZvopusK1iSs\nrlwHi4ZJfW4BXjH3lKFL0bov9Z4Gzs21ev+LI2PSujM3N5cuugjoh+sX9z4bM/jM/siVWybZB5l3\nfaV92vCxC3nmWc8290iRh5aV2cXLFT3QLp+xxGhWxjR6QbKFjrIynOxH/71dcm+iV8E9mccdd1yN\nTUfAJHVnbm5OzzzzzED2zz77bEmLvbrvf//7R/vgCUNmXS94NkcZ8jwT5DGTz5hzko2JjAV79uyR\n1EdlSNI73/lOSX1+yxe+8IXRtr/8y7+UJN1+++2D87h8xjK9DrrCtswLv5xyt8h/5ombFEdyto/o\nUOLIIzoUQ/bJyXSpKI5pSm+KYnWU7hTF6ijdKUasaAGvruv+RdK/LHz+tqTXT75LRXFsUXpTFKuj\ndKcoVkfpTrEUU7dyb0Ysf+auI8BVhFsqK2uYuUtwS5EI4u7LWP6TsIAsUSuuruv7ERZBeIWHPHA8\nq0HSH6kPeYiJnFkiYrYqMK6qGKok5avJFdNLLEsYQ3uijvh3WTgLMoTcXHHFFZL6ZFupT3winO41\nr3nNaBurhpKU6+U4Ce3BnUq/s4T7Sy65RFIfFuTXRmLglVdeKalfxdTb5xp9tVyS6u++++5BfzzE\njnAJdMz1Prqh3dWLDqN3hD+98pWvXHR8MR1s2rQpTXAfl2jHved57mWaY5gBuuTySelaQl1c9mLC\nsI8ntBPHFdcdwnkIN8tCfehTTCT2dtmWrRYf289ClbKQhPgcojCAn5v2eHb5c+3444+vUJ8poes6\nzc/PD2QXuOc8D/2dhOdtLDwi9frAO1EMicvacNAV/nq7WaEGKX8X5N3oPe95z2jbW97yFknSJz7x\nCUnSxz72MUnDohaENmWJv0uVjR8XzuPEpN5x4YlHymQDh4qiKIqiKIqimEqmzuKfzWxi2bBsMZWY\niOczMmasMdlW6hOOmMn5QkRexkrqS6u5hcbLlUlDyxB9ov24sIPUz5g5jtKfUp/wGy1TboHkerNE\nEr7zxBmoMp6zRWsttdpFy15micjuMdb/yy67TJL0oQ99SNKwrNob3/hGSdLNN98sSfrUpz61qP07\n77xTUl8yU+qt7yThoyMum1hRkE230qDTt912myTp3nvvHXwv9eXkuF5f+A4dRk9JoEd/pV7fsqTo\nmNSPHnofsLZSui5b5KtYfzZv3qwXv/jFA09s9ADH++2fuZd+T2N5ZUAmpN7STYEF34asjyvbt1RZ\nTalPrGR88rUK0Cv2z7zd4xbjioUAsn3jwkTe/1jq1K2l0XsRy2xLhyzCZfGfLlx2YtRBtgAX+2Dd\nHxddwDjk3uiYUO/yFUvYet+QJ46PCfrez1iS1rddffXVkqSLLrpIkvSRj3xktA9jElEnrpfR05Dp\nR/wtxr1/lcW/KIqiKIqiKIojYuos/uAznGgZ8XKWxDRiOcwsiFhrsD54+TRmjMSleVlASn1ikcBy\n6fHIWBqxuvjMN5Zii4vCeBvg8ZrMmGmDGaxb8OkT7WfW/VjqVCpL/6wR71f0XmWlz7CKuMUD0AWs\n48iae7k4N/H7bpX5m7/5G0n9wkLuKUCG0U0sN348ckq/sWJKixf4Qf594T1ycthGzL9f92mnnSap\nf174b4MFFt10ay86hYfOc4q4TvqLLnv/s9+7WB82b96sk046afBcjBbBzDIYY4ndsoiuIM/IiY8L\neIlo1z1SWMMZa1x2otWQsce/Z/+4EBnX63+j50DqZZ2/rhcx/j4rmxhLEfpvw/OD689yAzgXeur7\nVIz/9DA/P6/nnnsuXcAQYmnYeLyUewPYFvNNpF7W0TM/flxOY8x3i2XU4/7S0JPHcURdkO923XXX\njfb57d/+bUnSv/7rv0oajg1L5dtleXfZ/2MZUD9uOYuJrYSy+BdFURRFURTFBmDqLP7jlv+OC59I\n/QwwLkiULWSEJdHbIA45W9CIpaOxYG7fvl3S0CvAjJFZqVuNYoxajPmXegsi32WeijiT9P7H3Aaf\ngbKN85QlZXZZylKAvGRWZqwiWNYy3WJBKir3nHnmmaNtWDCRfyr4SH384+c//3lJw9wUKprwF4+T\n58NEa6F7DM4444zBOaku5BZ/ZBmPhZ8bb0CsxsW1Sr2+04+sUhdVieiPXxPbMo9BWfynhy1btujk\nk08eyAfyGC3fmQzwXHerNBb3eLx7tGiDHLKzzjprtI1ck2w8wsqJXPHs9/EMeWY8It/Fj8uqpMR9\neC74ufkcZThb/JF93UuNxT/m3jnRmxB/26o4Nx08/fTTuvbaawfeVCIjkGG2+fMb2R+36NS4vMXo\nzc7i6NE5f1/iHQodyipaxe98gbn4voUse8TExz/+cUl9NSCv5IV3Ly56ly3gFX+HtaYs/kVRFEVR\nFEWxAagX/6IoiqIoiqLYAExdqA9kCRGZCxD3CsmKmXspunfcdYTLCNc/7lOpL0v46KOPSurdXN5G\nTDj2ZBHOjTsJ96mHCpH0Rfk3T6ThN+DacM16GTnCMWIZOG8/S+Kq5N7ZgcW7XG6jLGfJP+NKn/GZ\nhaxuv/12SUrLHnJuL8331re+VZJ06623SurLekrSN7/5TUm9bKJTXk6T0At002Ualy19Y9vpp58+\n2udVr3qVpDxBkn5yHhJ5PcQu/m5+3ZzrwgsvlDQsl8jvjos7K8lYTA+bN2/WySefPEi8RWZiOI+H\na/Ed99VDZ2KYQrbIVSzpSviZJO3atUvSMBkYOBcyT7t+PsI5Gfu838gqYUfIt4chxeeBh0vEEKNs\n7OU42vJSpXGfbMyOJUJjQnOF+kwHW7du1fbt20fhalL/LsQ9i6VdpX5RRELQ/LlNSBDP26zwQlzY\nLnuny8JoYqnODLbRvoePMj6gT8i+F2DhWj760Y9Kkq666qpF544JzNn74rik+XiN8fMkKIt/URRF\nURRFUWwA2lomfLbWfiRpv6SnD7fvlHGqZq/P0mT6fUbXdT9/+N2Ko8WC3jym2ZTDWeyzVLpzTDDD\nY440m7pTenOMMMO6M4t6I62h7qzpi78ktdbu6Lpu75o2eoTMYp+l2e13kTOL93MW+yzNbr+Lxczq\nvZzFfs9in4ulmcX7OYt9lta23xXqUxRFURRFURQbgHrxL4qiKIqiKIoNwHq8+F+zDm0eKbPYZ2l2\n+13kzOL9nMU+S7Pb72Ixs3ovZ7Hfs9jnYmlm8X7OYp+lNez3msf4F0VRFEVRFEWx9lSoT1EURVEU\nRVFsAOrFvyiKoiiKoig2AGv24t9a+/XW2kOttUdaa3+4Vu2ulNbay1prX2qtPdBau7+19rsL35/c\nWvtia+3hhb8nrXdfI621za21u1tr/7jw/7Naa/sWfvNPt9aOW+8+FiundOfoU7pzbDILulN6U0wb\ns6A3UunOalmTF//W2mZJ/0fSmyXtkfRfW2t71qLtVXBQ0tVd1+2RdLGk/7bQ1z+UdGPXda+QdOPC\n/6eN35X0oP3/Y5L+vOu6syU9I+kD69KrYtWU7qwZpTvHGDOkO6U3xdQwQ3ojle6sirWy+L9e0iNd\n132767qfSbpO0tvXqO0V0XXd97uuu2vh83M6dGN26lB/r13Y7VpJ71ifHua01nZJeoukTyz8v0l6\ng6T/u7DL1PW5WBalO0eZ0p1jlpnQndKbYsqYCb2RSndWy1q9+O+U9D37/+ML3001rbUzJV0oaZ+k\n07qu+/7CpqcknbZO3VqKj0v6A0nzC/8/RdKzXdcdXPj/TPzmxSJKd44+pTvHJjOnO6U3xRQwc3oj\nle6shEruXYLW2osl/b2k3+u67se+rTtUA3Vq6qC21t4q6Ydd19253n0pitKdolg5pTdFsTpKd1bG\nljVq5wlJL7P/71r4bipprW3VISH6VNd1n1v4+gette1d132/tbZd0g/Xr4eLuFTS21prvyHpBEkv\nkfQXkra11rYszCKn+jcvlqR05+hSunPsMjO6U3pTTBEzozdS6c5qWCuL/+2SXrGQtXycpP8i6R/W\nqO0VsRBr9UlJD3Zd92e26R8kvW/h8/skXb/WfVuKruv+qOu6XV3XnalDv+1NXdf9lqQvSbpyYbep\n6nOxbEp3jiKlO8c0M6E7pTfFlDETeiOV7qyWNXnxX5jB/HdJN+hQ8sVnuq67fy3aXgWXSnqvpDe0\n1u5Z+Pcbkv5U0hWttYclvXHh/9PORyT9fmvtER2KIfvkOvenWCGlO+tG6c6MM0O6U3pTTA0zpDdS\n6c6qaIfCn4qiKIqiKIqiOJap5N6iKIqiKIqi2ADUi39RFEVRFEVRbADqxb8oiqIoiqIoNgD14l8U\nRVEURVEUG4B68S+KoiiKoiiKDUC9+BdFURRFURTFBqBe/IuiKIqiKIpiA/D/Abx72jjqYdFwAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1953ca67588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(0, figsize=(12,6))\n",
    "for i in range(1, 13):\n",
    "    plt.subplot(3,4,i)\n",
    "    plt.imshow(x_train[i, :, :, 0], cmap=\"gray\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 46, 46, 64)        640       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 46, 46, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 46, 46, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 44, 44, 64)        36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 44, 44, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 44, 44, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 20, 20, 32)        18464     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 20, 20, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 20, 20, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 18, 18, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 18, 18, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 18, 18, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 16, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               262272    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 7)                 903       \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 7)                 0         \n",
      "=================================================================\n",
      "Total params: 339,111\n",
      "Trainable params: 338,407\n",
      "Non-trainable params: 704\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(64, 3, data_format=\"channels_last\", kernel_initializer=\"he_normal\", \n",
    "                 input_shape=(48, 48, 1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Conv2D(64, 3))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPool2D(pool_size=(2, 2), strides=2))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Conv2D(32, 3))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Conv2D(32, 3))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Conv2D(32, 3))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPool2D(pool_size=(2, 2), strides=2))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Dense(7))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 28709 samples, validate on 3589 samples\n",
      "Epoch 1/10\n",
      "Epoch 00000: val_loss improved from inf to 1.25328, saving model to face_model.h5\n",
      "197s - loss: 1.3192 - acc: 0.4920 - val_loss: 1.2533 - val_acc: 0.5188\n",
      "Epoch 2/10\n",
      "Epoch 00001: val_loss improved from 1.25328 to 1.24994, saving model to face_model.h5\n",
      "197s - loss: 1.2981 - acc: 0.5010 - val_loss: 1.2499 - val_acc: 0.5194\n",
      "Epoch 3/10\n",
      "Epoch 00002: val_loss did not improve\n",
      "198s - loss: 1.2771 - acc: 0.5104 - val_loss: 1.2728 - val_acc: 0.5138\n",
      "Epoch 4/10\n",
      "Epoch 00003: val_loss improved from 1.24994 to 1.19144, saving model to face_model.h5\n",
      "197s - loss: 1.2616 - acc: 0.5185 - val_loss: 1.1914 - val_acc: 0.5419\n",
      "Epoch 5/10\n",
      "Epoch 00004: val_loss did not improve\n",
      "197s - loss: 1.2464 - acc: 0.5202 - val_loss: 1.2052 - val_acc: 0.5394\n",
      "Epoch 6/10\n",
      "Epoch 00005: val_loss improved from 1.19144 to 1.18031, saving model to face_model.h5\n",
      "198s - loss: 1.2312 - acc: 0.5300 - val_loss: 1.1803 - val_acc: 0.5486\n",
      "Epoch 7/10\n",
      "Epoch 00006: val_loss improved from 1.18031 to 1.15327, saving model to face_model.h5\n",
      "199s - loss: 1.2116 - acc: 0.5375 - val_loss: 1.1533 - val_acc: 0.5592\n",
      "Epoch 8/10\n",
      "Epoch 00007: val_loss did not improve\n",
      "201s - loss: 1.2034 - acc: 0.5405 - val_loss: 1.1667 - val_acc: 0.5495\n",
      "Epoch 9/10\n",
      "Epoch 00008: val_loss did not improve\n",
      "198s - loss: 1.1862 - acc: 0.5463 - val_loss: 1.2024 - val_acc: 0.5277\n",
      "Epoch 10/10\n",
      "Epoch 00009: val_loss improved from 1.15327 to 1.14648, saving model to face_model.h5\n",
      "228s - loss: 1.1891 - acc: 0.5475 - val_loss: 1.1465 - val_acc: 0.5637\n"
     ]
    }
   ],
   "source": [
    "# save best weights\n",
    "checkpointer = ModelCheckpoint(filepath='face_model.h5', verbose=1, save_best_only=True)\n",
    "\n",
    "# num epochs\n",
    "epochs = 10\n",
    "\n",
    "# run model\n",
    "hist = model.fit(x_train, y_train, epochs=epochs,\n",
    "                 shuffle=True,\n",
    "                 batch_size=100, validation_data=(x_test, y_test),\n",
    "                 callbacks=[checkpointer], verbose=2)\n",
    "\n",
    "# save model to json\n",
    "model_json = model.to_json()\n",
    "with open(\"face_model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FbSF8/foWWP30EwQ1fI96+/ZZgYobbrB1KRHMS6q7VDtyxNZcvfACrFljs/C9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\nGWOMMcYYY0wWWFJljDHGGGOMMVnw/0QTT9zO10IBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x195ce86ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,3))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.suptitle('Optimizer : Adam', fontsize=10)\n",
    "plt.ylabel('Loss', fontsize=16)\n",
    "plt.plot(hist.history['loss'], color='b', label='Training Loss')\n",
    "plt.plot(hist.history['val_loss'], color='r', label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.ylabel('Accuracy', fontsize=16)\n",
    "plt.plot(hist.history['acc'], color='b', label='Training Accuracy')\n",
    "plt.plot(hist.history['val_acc'], color='r', label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emotion</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32298</th>\n",
       "      <td>0</td>\n",
       "      <td>[170.0, 118.0, 101.0, 88.0, 88.0, 75.0, 78.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32299</th>\n",
       "      <td>5</td>\n",
       "      <td>[7.0, 5.0, 8.0, 6.0, 7.0, 3.0, 2.0, 6.0, 5.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32300</th>\n",
       "      <td>6</td>\n",
       "      <td>[232.0, 240.0, 241.0, 239.0, 237.0, 235.0, 246...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32301</th>\n",
       "      <td>4</td>\n",
       "      <td>[200.0, 197.0, 149.0, 139.0, 156.0, 89.0, 111....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32302</th>\n",
       "      <td>2</td>\n",
       "      <td>[40.0, 28.0, 33.0, 56.0, 45.0, 33.0, 31.0, 78....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       emotion                                             pixels\n",
       "32298        0  [170.0, 118.0, 101.0, 88.0, 88.0, 75.0, 78.0, ...\n",
       "32299        5  [7.0, 5.0, 8.0, 6.0, 7.0, 3.0, 2.0, 6.0, 5.0, ...\n",
       "32300        6  [232.0, 240.0, 241.0, 239.0, 237.0, 235.0, 246...\n",
       "32301        4  [200.0, 197.0, 149.0, 139.0, 156.0, 89.0, 111....\n",
       "32302        2  [40.0, 28.0, 33.0, 56.0, 45.0, 33.0, 31.0, 78...."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = df[[\"emotion\", \"pixels\"]][df[\"Usage\"] == \"PrivateTest\"]\n",
    "test[\"pixels\"] = test[\"pixels\"].apply(lambda im: np.fromstring(im, sep=' '))\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_test_private = np.vstack(test[\"pixels\"].values)\n",
    "y_test_private = np.array(test[\"emotion\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3589, 48, 48, 1), (3589, 7))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_private = x_test_private.reshape(-1, 48, 48, 1)\n",
    "y_test_private = np_utils.to_categorical(y_test_private)\n",
    "x_test_private.shape, y_test_private.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1194702833932433, 0.56673168016695719]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.evaluate(x_test_private, y_test_private, verbose=0)\n",
    "score"
   ]
  }
 ],
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